The memory/storage names: change vs Friday's close and intraday range

Name: The memory/storage names: change vs Friday's close and intraday range
Creator: Strasmore
Published: 2026-07-26
License: https://www.strasmore.com/terms/

Answered against 22 years of US equities and 12 years of US options data and published with the query that produced it. This result is stored as of 2026-07-26, from Market Recap: June 29, 2026, The Day in Numbers.

as of 2026-07-26 table 4×9read in context →

The memory/storage names: change vs Friday's close and intraday range — 4 rows by 9 columns, computed 2026-07-26 from US exchange, SIP and OPRA data.
ticker	jun26_close	jun29_close	pct_chg	day_high	day_high_et	day_low	day_low_et	range_pct
MU	1122.92	1145	1.97	1148.79	15:59	1023.65	10:18	12.22
SNDK	2091.08	2051.29	-1.9	2090.71	09:30	1895	10:18	10.33
STX	895.27	968.4	8.17	987.57	14:48	880.01	10:00	12.22
WDC	586.32	651.7	11.15	652.98	15:59	590	09:31	10.67

Rows × columns: 4 × 9
Computed: 2026-07-26
Completeness: No missing values
Source: US exchange, SIP and OPRA market data
Licence: Strasmore terms · free, no signup
Formats: JSON · CSV · the SQL below

What each column holds

Column definitions for The memory/storage names: change vs Friday's close and intraday range, derived from the stored result.
Column	Type	Range	Notes
`ticker`	text	4 distinct values (MU, SNDK, STX…)
`jun26_close`	number	586.32 to 2,091.08	US dollars
`jun29_close`	number	651.7 to 2,051.29	US dollars
`pct_chg`	number	-1.9 to 11.15	percent
`day_high`	number	652.98 to 2,090.71	US dollars
`day_high_et`	text	3 distinct values (09:30, 14:48, 15:59)
`day_low`	number	590 to 1,895	US dollars
`day_low_et`	text	3 distinct values (09:31, 10:00, 10:18)
`range_pct`	number	10.33 to 12.22	percent

Computed from Strasmore's warehouse of US exchange, SIP and OPRA market data. Equity prices are delayed; options greeks and implied volatility are end-of-day. This result is stored, not recomputed on load — it is exactly the numbers that were returned on 2026-07-26, and the query below is what returned them.

Run it yourself

This is the exact query behind the result above. Change a ticker, a date or a column and run it against the warehouse — no account, no key. The no-signup tier is smaller than the one this page was computed on; a query that reaches past it comes back saying which plan runs it.

WITH per_name AS (
    SELECT
        ticker,
        toFloat64(argMaxIf(close, window_start, window_start < '2026-06-27 00:00:00')) AS friday_close,
        toFloat64(argMaxIf(close, window_start, window_start >= '2026-06-29 00:00:00')) AS monday_close,
        maxIf(toFloat64(high), window_start >= '2026-06-29 00:00:00') AS day_high,
        minIf(toFloat64(low), window_start >= '2026-06-29 00:00:00') AS day_low,
        argMinIf(window_start, toFloat64(low), window_start >= '2026-06-29 00:00:00') AS low_bar,
        argMaxIf(window_start, toFloat64(high), window_start >= '2026-06-29 00:00:00') AS high_bar
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('MU', 'SNDK', 'STX', 'WDC')
      AND ((window_start >= '2026-06-26 13:30:00' AND window_start < '2026-06-26 20:00:00')
        OR (window_start >= '2026-06-29 13:30:00' AND window_start < '2026-06-29 20:00:00'))
    GROUP BY ticker
)
SELECT
    ticker,
    round(friday_close, 2) AS jun26_close,
    round(monday_close, 2) AS jun29_close,
    round((monday_close / friday_close - 1) * 100, 2) AS pct_chg,
    round(day_high, 2) AS day_high,
    formatDateTime(toTimeZone(high_bar, 'America/New_York'), '%H:%i') AS day_high_et,
    round(day_low, 2) AS day_low,
    formatDateTime(toTimeZone(low_bar, 'America/New_York'), '%H:%i') AS day_low_et,
    round((day_high / day_low - 1) * 100, 2) AS range_pct
FROM per_name
ORDER BY ticker

SQL to run

WITH per_name AS (
    SELECT
        ticker,
        toFloat64(argMaxIf(close, window_start, window_start < '2026-06-27 00:00:00')) AS friday_close,
        toFloat64(argMaxIf(close, window_start, window_start >= '2026-06-29 00:00:00')) AS monday_close,
        maxIf(toFloat64(high), window_start >= '2026-06-29 00:00:00') AS day_high,
        minIf(toFloat64(low), window_start >= '2026-06-29 00:00:00') AS day_low,
        argMinIf(window_start, toFloat64(low), window_start >= '2026-06-29 00:00:00') AS low_bar,
        argMaxIf(window_start, toFloat64(high), window_start >= '2026-06-29 00:00:00') AS high_bar
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('MU', 'SNDK', 'STX', 'WDC')
      AND ((window_start >= '2026-06-26 13:30:00' AND window_start < '2026-06-26 20:00:00')
        OR (window_start >= '2026-06-29 13:30:00' AND window_start < '2026-06-29 20:00:00'))
    GROUP BY ticker
)
SELECT
    ticker,
    round(friday_close, 2) AS jun26_close,
    round(monday_close, 2) AS jun29_close,
    round((monday_close / friday_close - 1) * 100, 2) AS pct_chg,
    round(day_high, 2) AS day_high,
    formatDateTime(toTimeZone(high_bar, 'America/New_York'), '%H:%i') AS day_high_et,
    round(day_low, 2) AS day_low,
    formatDateTime(toTimeZone(low_bar, 'America/New_York'), '%H:%i') AS day_low_et,
    round((day_high / day_low - 1) * 100, 2) AS range_pct
FROM per_name
ORDER BY ticker

⌘/Ctrl + Enter

Work with this data in your AI assistant

Opens ready to query, with this page's data. Free, no account.

Claude ChatGPT Gemini Muse Your own agent

More from this analysisMarket Recap: June 29, 2026, The Day in Numbers

The eleven sector baskets: June 29 vs the June 26 close, regular hours table 11×6 → Volume leaders two ways: top 6 by dollars traded, top 4 by shares traded table 10×6 → SPY / QQQ / DIA / IWM: June 29 vs the June 26 close, regular hours table 4×9 → What it cost to cross the spread: NBBO updates and median quoted width, regular hours table 4×7 → Shares traded per 30-minute bucket, regular hours, closing auction prints in the final bucket (billions) series 13×4 → The Treasury curve, June 29 close vs June 26 (populated maturities only) ranking 8×3 → See all 2,985 queries →